Introduction to Professor Hoi To Wai and His Lab at Chinese University of Hong Kong

1. Could you briefly introduce yourself (and your University/Lab)?

I have been an assistant professor at the department of Systems Engineering & Engineering Management of the Chinese University of Hong Kong (CUHK) since Spring 2019, after spending six wonderful years in the US where I obtained my PhD from Arizona State University. Besides, I have been a CUHK-er since 2006.  My research group at CUHK is working on various aspects of network science – ranging from new techniques for graph learning, theories for graph signal processing, to theory of distributed optimization, and sometimes stochastic optimization.

More details can be found on my website: https://www1.se.cuhk.edu.hk/~htwai/

2. What have been your most significant research contributions up to now?

My research group has been advancing the theories of optimization and graph signal processing. On optimization theory, we recently worked on the convergence of various stochastic algorithms using dynamic data. These have important consequences in reinforcement learning, and they also lead to the development of faster algorithms for statistical learning such as expectation maximization, meta learning, etc.. On graph signal processing, we recently develop a number of algorithms that can provably learn the features of graph topology from limited data – ranging from low-rank data, to partially observed signals.

3. What problems in your research field deserve more attention (or what problems will you like to solve) in the next few years, and why?

The theories behind many machine learning and signal processing algorithms have received attention of varying levels in recent years, and they were sometimes dismissed by the practitioners. I believe that this research deserves the same level of attention as the practical algorithms that have enjoyed a lot of successes in improving our life. In fact, theory and empirical studies should work hand-in-hand as they complement each other in the advance of science.

4. What advice would you like to give to the young generation of researchers/engineers?

My advice is “stay passionate” to willingly work on your research topic, “be innovative” to think outside of the box and lead the others instead of being a follower, and “be critical” to maintain a high standard on your work through criticizing it yourself.